docs: def noelle_5(h1, h2): # 26 us @array, 52 us @list \w 100 bins
      r"""
      Extension of `fidelity_based` proposed by [1]_.
      
      .. math::
      
          d_{\sin F}(H, H') = \sqrt{1 -d_{F}^2(H, H')}
      
      See `fidelity_based` for the definition of :math:`d_{F}(H, H')`.
                  
      *Attributes:*
  
      - metric
      
      *Attributes for normalized histograms:*
  
      - :math:`d(H, H')\in[0, 1]`
      - :math:`d(H, H) = 0`
      - :math:`d(H, H') = d(H', H)`
      
      *Attributes for not-normalized histograms:*
  
      - not applicable
      
      *Attributes for not-equal histograms:*
  
      - not applicable
      
      Parameters
      ----------
      h1 : sequence
          The first histogram, normalized.
      h2 : sequence
          The second histogram, normalized, same bins as ``h1``.
      
      Returns
      -------
      fidelity_based : float
          Fidelity based distance.
      
      References
      ----------
      .. [1] M. Noelle "Distribution Distance Measures Applied to 3-D Object Recognition", 2003
      """
      return math.sqrt(1 - math.pow(fidelity_based(h1, h2), 2))